{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit-learn库\n",
    "\n",
    "scikit-learn已经封装好很多数据挖掘的算法\n",
    "\n",
    "现介绍数据挖掘框架的搭建方法\n",
    "- 转换器（Transformer）用于数据预处理，数据转换\n",
    "- 流水线（Pipeline）组合数据挖掘流程，方便再次使用（封装）\n",
    "- 估计器（Estimator）用于分类，聚类，回归分析（各种算法对象）\n",
    "  - 所有的估计器都有下面2个函数\n",
    "    - fit() 训练\n",
    "      - 用法：estimator.fit(X_train, y_train)，\n",
    "      - estimator = KNeighborsClassifier() 是scikit-learn算法对象\n",
    "      - X_train = dataset.data 是numpy数组\n",
    "      - y_train = dataset.target 是numpy数组\n",
    "    - predict() 预测\n",
    "      - 用法：estimator.predict(X_test)\n",
    "      - estimator = KNeighborsClassifier() 是scikit-learn算法对象\n",
    "      - X_test = dataset.data 是numpy数组\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/dlinking-lxy\n",
      "./data\n",
      "./data/ionosphere.data\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Ionosphere数据集\n",
    "# https://archive.ics.uci.edu/ml/machine-learning-databases/ionosphere/\n",
    "# 下载ionosphere.data和ionosphere.names文件，放在 ./data/Ionosphere/ 目录下\n",
    "import os\n",
    "home_folder = os.path.expanduser(\"~\")\n",
    "print(home_folder) # home目录\n",
    "# Change this to the location of your dataset\n",
    "home_folder = \".\" # 改为当前目录\n",
    "data_folder = os.path.join(home_folder, \"data\")\n",
    "print(data_folder)\n",
    "data_filename = os.path.join(data_folder, \"ionosphere.data\")\n",
    "print(data_filename)\n",
    "import csv\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Size taken from the dataset and is known已知数据集形状\n",
    "X = np.zeros((351, 34), dtype='float')\n",
    "y = np.zeros((351,), dtype='bool')\n",
    "\n",
    "\n",
    "with open(data_filename, 'r') as input_file:\n",
    "    reader = csv.reader(input_file)\n",
    "    for i, row in enumerate(reader):\n",
    "        # Get the data, converting each item to a float\n",
    "        data = [float(datum) for datum in row[:-1]]\n",
    "        # Set the appropriate row in our dataset用真实数据覆盖掉初始化的0\n",
    "        X[i] = data\n",
    "        # 1 if the class is 'g', 0 otherwise\n",
    "        y[i] = row[-1] == 'g' # 相当于if row[-1]=='g': y[i]=1 else: y[i]=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集数据有 263 条\n",
      "测试集数据有 88 条\n",
      "每条数据有 34 个features\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dlinking-lxy/more-space/pyworks/venv/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 数据预处理\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=14)\n",
    "print(\"训练集数据有 {} 条\".format(X_train.shape[0]))\n",
    "print(\"测试集数据有 {} 条\".format(X_test.shape[0]))\n",
    "print(\"每条数据有 {} 个features\".format(X_train.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率 86.4%\n",
      "平均准确率 82.3%\n"
     ]
    }
   ],
   "source": [
    "# 实例化算法对象->训练->预测->评价\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "estimator = KNeighborsClassifier()\n",
    "estimator.fit(X_train, y_train)\n",
    "y_predicted = estimator.predict(X_test)\n",
    "accuracy = np.mean(y_test == y_predicted) * 100\n",
    "print(\"准确率 {0:.1f}%\".format(accuracy))\n",
    "\n",
    "# 其他评价方式\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "scores = cross_val_score(estimator, X, y, scoring='accuracy')\n",
    "average_accuracy = np.mean(scores) * 100\n",
    "print(\"平均准确率 {0:.1f}%\".format(average_accuracy))\n",
    "\n",
    "avg_scores = []\n",
    "all_scores = []\n",
    "parameter_values = list(range(1, 21))  # Including 20\n",
    "for n_neighbors in parameter_values:\n",
    "    estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    scores = cross_val_score(estimator, X, y, scoring='accuracy')\n",
    "    avg_scores.append(np.mean(scores))\n",
    "    all_scores.append(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f13e8111c18>]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
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PZa8HmaS+3S09+vfPv2/E/T5HW+5o1LzZ3hZ/\npPG7KN8+3k03UJZo7xvSywfMnwGb19d5kE28ZZYRr0fOXFA0ST8PkZ2sHZQ1H1+gXL+wzIjtbe3V\ncII/SAYAAAAAAJCpKFCmiRf2d2ggQbsnxxoIR/ViU0dSzkbmsXVQHuzo12A44kE2qevM+WHd/+Qu\nhS3dyf/+syt01aI5HmRld8uqKi2uKDTiD71y1INsMNOefadVY+t91SW5um5ZuTcJfUR5UY5K84Nx\nsXAkpuZzAx5lhEzTPzyqZsse5WVVH1+gXFCWr/KinLhYOBLTu6f6EpofAAAAAABApqJAmSaSveOP\nEZ2YqMriXM0tzYuLRWMuN2U/YiQS1X1P7dKZ/hHjsdsaa/XbVy2Y+aQ+hs/n6IHrFxnx1w936d1T\ndMdmskg0pmffaTXiX71yngJ+739FcBxHSy1dlIx5RaLYvpYWlOWrMOfjx287jmPdQ7njBPt7AQAA\nAAAAJsL7u4+YkPa+4eSe35vc85FZbF2UjHn9gOu6+uPn91v/e6yuLdF//dJlchzHg8w+3uevqFGd\nZeQsXZSZ7bVDXcbPF7/P0deu9H6860XLbXsoOylQIjGmMt71IvZQAgAAAAAATB0FyjQRSfK+rWSf\nj8xi30NJF64kPfV2s57daXakzSnM0SN3rVVu0O9BVpcW8Pv0jevMLsoXmzp1sMO8gY/M8PT2ZiN2\nw/IKVZXkepCNna2D8jAFSiRIU5t5fVtxifGuF9k6KHc19yhiGe0NAAAAAACAeBQo00TAl9yOq2Sf\nj8xi66Dc3dIr183uQvf24+f0n/6+yYgH/Y4eubNR1SV5lleljt9srFW1pTC15dVjHmSDZDvVM6jX\nDncZ8c0bUqd7UpKW2TooGfGKBDkwjQ7KpRVFKsmL35E6GI5qv6XoCQAAAAAAgHgUKNOErWiQ0PNL\nU6dbBqlvVU2xQmP20529MKJTPUMeZeS9071D+sbTu63dyP/pC5dp3QKz0ybVhAI+3b/J7KL8ybtt\nOtZ1wYOMkEzPvtOqsZ8pmFuap41Lyr1JaBxLK8wC5clzgxoejXqQDTJJJBrTQUuxe6IFSp/P0ZWW\na/sOxrwCAAAAAABcEgXKNGEbqZlIjUk+H5klJ+DXqrnmDdw9rdm5h3J4NKr7ntypcwNh47E7NtSl\nXEfax/nqlfM0pzAnLua60l++RhdlJhmNxvTsO+Yo4s0b6uRPsY76kvyg8SGdaMylaI5pO3F2QCOR\n+HGspflBVRVP/ENb7KEEAAAAAACYGgqUaeKWVVUqCCVnd11ByK+bV1Yl5WxkroZ5ZlF7d3P27aF0\nXVd/8Ny7ev+0OdLvygWz9B8+v8qDrKYuN+jXvRsXGvG/23Nard2DHmSEZHj5QKfO9I/ExQI+R19Z\nV+tRRh+PPZRIhibbeNfqYjnOxIv0tj2U75zsVozd3gAAAAAAAB+LAmWauGllpXxJ6mrx+xzdvKoy\nKWcjczXON/dQZmMH5eNvntDze9uMeFVxrh6+Y61CgfS7zN6xYb5K8+P3qkVjrh55nS7KTPH09hYj\ndtPKSlUUpea4b/seSjooMT3jFSgnY1VNsfLHfICsb2hUh89QQAcAAAAAAPg46XfnPEuFAj7dt6le\necHEdlHmBX26d1O9gn6+FDA5trHDTW19WbUX7o0jXfrTnx4w4qGAT4/dvVblRTmWV6W+gpyA7rna\n7KL8/s5T6jw/7EFGSKSWc4N648hZI37HhvkeZDMxyywdlIc6zOISMBlNbebX0IpJFigDfp/Wzjd/\nHrKHEgAAAAAA4ONRlUoj929cpJrSXCWqj9JxpJrSPN2/cVGCTkQ2qSnJVcWYAtxo1NX+tj6PMppZ\nzecG9ODWPbJN8ftvX7pcV9SaHabp5O6rFqgoJxAXC0djemzbcY8yQqJs3WF2T84vy9dVi8o8yGZi\nbB2UhzvpoMT0HGg3uxxX1kyuQClJ6xewhxIAAAAAAGCyKFCmkYDfp0fuXKu8BO2izA/69cidaxWg\nexJT4DiOGi1dlHtaMn/M68BIRPc+sUt9Q6PGY//06oW6bW1q7vGbjJK8oP6/qxYY8ae3N+vchRHz\nBUgL4UhMP9jVasRvX1+XtDHiibC4olBj1wKe7h1S/7D5PQhMxJn+YZ0dcy0L+X1aVF446bNseyh3\nnOiW67KHEgAAAAAAYDxUptLMksoiPXnPehWE/FPupHQkFYT8euKeDVpiGZsHTFRDnWUPZYYXKF3X\n1b/+/j4d6jQ7b65eXKY//MxyD7JKjn96zUJjrPTwaEyPv3nCo4wwXS82dejshXBcLOh39OUUL6rn\nBv1aUFZgxOmixFTZxrsuriic0t7g1fNKFRrzYa+u/hGdPDc45fwAAAAAAAAyHQXKNLR2/mw9/8DV\nqi8vUF5wcn+FeUGf6ssL9PwDV1t3JgGTYdtDubulx4NMZs6WV4/qH9/vMOK1s/L00O2NGdWRPLsg\npDs21BnxJ37ZrL5BOtfS0dbt5njXT19WrTmFqb8v1b6H0vygADARiRrvKn1QQF8zz/zAzo4T56Z0\nHgAAAAAAQDbInDvpWWZJZZF+9q2NeuCGxSrODajgEmNfC0J+FecG9OANS/TCtzbSOYmEuHxuiQJj\nxkK29w2rvW/Io4yS6+UDnfqLlw4b8bygX3919zrNKgh5kFVyfX1jvdFRdGEkor/9xUlvEsKUHe+6\noF8cMwsmm9ebRehUtNS6h5ICJaamqd3soFxZPbUCpWQf88oeSgAAAAAAgPFRoExjQb9PD16/RLv+\n6Cb9+ZdX69Y1NZo3O+9XBaOAz9G82Xm6dU2N/vtXVmvXH92kB65fnFEdXvBWXsivFZYbunszcMzr\n0TMX9K3v7pVtpdj/+Mpq63+HTFBZnKuvrptnxP/6rRO6MBLxICNM1TM7zO7J+vICfaLeLKykIjoo\nkUhNbX1GbDrX8fH2UAIAAAAAAMAu4HUCmL6g36fPXlGtz15R7XUqyEINdaV673T8jd7dLT36jcsz\n5+vx/PCo7n1yp/otBblvXLco47/37ttUr2d2tCgS+3V1tm9oVE+93az7Ny3yMDNM1PBoVD/YdcqI\nb15fJ8eZ6kbjmbWsqtCI0UGJqRgKR3Xi7IARn04HZeP8WfL7HEU/cp081TOk071DmluaN+VzAQAA\nAAAAMhWtdACmpdGyh3JPBnVQxmKuvvXdvTreZd7Mvn5Zuf7Vzcs8yGpm1c7K1282zjXi/+eN4xoK\nRz3ICJP1wv4O9YzZGxoK+HRbY61HGU3egrIChcZMADg3EFZX/4hHGSFdHersV2xMN/zc0jyV5Aen\nfGZhTkCXWXZYvkMXJQAAAAAAgBUFSgDT0lBXasTeO92ncCTmQTaJ9z9fOqxXDp4x4vVzCvTtrzXI\n70uP7rPp+p3rFmvsH/XshbC++445NhSp5+m3zb+nz15enVZ7UwN+nxZV0EWJ6WtqM/dPJmJMN3so\nAQAAAAAAJo4CJYBpqZudr9ljihwjkZgOtJs3gNPNT99r10OvHjXihTkBPXb3WpXkTb3bJt0snFOg\nz6+uMeKPvn5cIxG6KFPZkc5+7ThpFkk2b6jzIJvpWVZpFijZQ4nJsv18Wmnpfpys9QvLjNiOE+em\nfS4AAAAAAEAmokAJYFocx1GjpYtyT0uPB9kkzsGO8/rX399nfex/fXWNFlcUzXBG3nvg+sVGrOP8\nsJ7bddqDbDBRW3eY3ZNLKwu1br45njnVLa0yv+/ooMRkNdkKlAnooLxygfk9daxrQGcvMIYYAAAA\nAABgLAqUAKatwbaHsjV991D2Dob19Sd2atCyX/F3b1qqm1ZWepCV95ZWFunTq6qM+F++flSRaGaM\n9M00w6NRPbfrlBHfvL5OjpN+44mXWwqUB+mgxCTEYq69gzIBBcrS/JD1a5Q9lAAAAAAAACYKlACm\nrWGe2UG5O007KCPRmB7cuket3UPGY7esqtSDli7CbPLgDeafv7V7SD/e1+ZBNriUn7zbrvPDkbhY\nbtCnLzXWepTR9CytNIs/Rzr7FYu5HmSDdNTSPWh8+KQoJ6DaWXkJOd+2h9I2YhkAAAAAACDbUaAE\nMG1XzCuVb0wzVmv3kLr602+s3Z/97KDePHrWiC+tLNRf/NYa+cb+QbPMZXNLdP2yciO+5dWjilIk\nSjlbtzcbsc9dUZO2+1PnluapIOSPiw2Eozrda36gALCxjXddUV2csGu7tUBJByUAAAAAAICBAiWA\naSvMCVg7m/am2ZjX5/ec1l+9ccKIF+cG9Nhd61SYE/Agq9Tz4A1LjNixrgH97P0OD7LBeA60n9fu\nFvN78I4NdR5kkxiO47CHEtPS1GYrUCZup/D6BWaBsqn9vM4PjybsPQAAAAAAADIBBUoACWHbQ5lO\nY17fO9Wn33/uXSPuc6TvbG7UgjkFHmSVmtbOn6WrFpUZ8e+8ckSuSxdlqti6vcWIragu1hrLSOZ0\nwh5KTId1/2TN9PdPXlRRnKuFY35euK6062T6/DwEAAAAAACYCbQDAUiIxrpSPbMjviCyJ00KlF39\nI7r3yZ0aicSMx37/08u1aak50jTbPXj9Yv3i2Lm42MGOfr184Iw+tbLSo6xw0WA4ouf3nDbimzfU\nyXHSe0yxrVubDkpMlG3E68rqkoS+x/oFs3Xi7EBcbPuJbl2/vCKh7wOEIzG91NSpF/Z3aE9Lj9r7\nhhWJuQr4HFWX5KqhbpZuWVWlm1dVKujnc6kAAAAAgNRCgRJAQtg6KN891adINKZACt8UC0dieuDp\n3WrvGzYe+8LqGt27sd6DrFLfJxeVqbGu1Bgh+p1Xj+rGFRVpXwRLd3+/r039I5G4WH7Iry+uqfEo\no8RZZilQHqKDEhPQMxA2rvV+n6MllYUJfZ/1C2fr2Z2tcbEdJ86N82xg8kajMT267Zgeff24Yq6r\ngZFo3OORmKvWniG19gzp5QOd+oMfOrpvU73u37gopX8nAwAAAABkF/6FCiAh6ucUqDg3/jMPg+Go\nDqV4Z9N//kmTdpzsNuKraor1Z7ddQaFtHI7j6JuWXZT7Wnv15tGzHmSEj7KNd711TY2KcoMeZJNY\nth2Ux7sGNBo1O6CBj7KNd11UXqDcoD+h77N+obmH8t1TfRoKRy3PBibnSGe/bvn2Nm155Zj6hyNG\ncXKsgXBU/cMRbXnlqG759jYdSfHfywAAAAAA2YMCJYCE8PkcaxflnjEddqnkuzta9OTbzUZ8dkFI\nj961VnmhxN60zjTXLSvXKsvutu+8ctSDbHDR+6f7tO9UnxHfvH6+B9kk3pzCHM0pDMXFwtGYTo4Z\nqQmMZR/vmrj9kxfVzspTTUluXCwSc9Nm7DlS167mbt265S2d6BrQ0OjkCt5DozEd7xrQF7e8pV3N\n5gezAAAAAACYaRQoASRMQ12pEUvVAuWu5m790Y/eN+J+n6OH72hU7ax8D7JKLx90US424jtOdGv7\nccYZeuVpS/fk5XNLdHltYvfsecm2hzLVu7XhvaY2s0C5IgkFSsdxrF2U209QFMLUHens112P79Bg\nOCp3ime4+qCj8u7Hd9BJCQAAAADwHAVKAAlj76BMvY6Rjr5h3f/Ubo1GzVt8f/y5lfpEfZkHWaWn\nm1dWaUmFub/toVfpovTChZGIfrz3tBG/Y0OdB9kkj61AeZg9lLgEawelpQs8EdYvNH+O7KBAiSka\njcZ031O7EjYmeDAc1f1P7WI0NgAAAADAUxQoASTMmnlmB+XxswPqGQh7kI3d8GhU9z21S139I8Zj\nv7WuVnd/MjPGYM4Un8/Rg5YuyjeOnNXe1tTsns1kP9p7WgNjbmAX5gT0+dU1HmWUHMsseygPUqDE\nxxiJRHX0zAUjnowOSsm+h3J3S4/CEQpCmLxHtx1Te+/wlDsnx3IltfUO6dFtxxJ0IgAAAAAAk0eB\nEkDClOQFtdjSTbf3VGoUqlzX1R89/772WQpna+aV6j9/8TI5juNBZunts5dXa0GZORL3IXZRzijX\ndfX02+Z41y821KggJ+BBRsljK1AeZlwhPsaRzguKxOLLOxVFOZpTmJOU91tUXqCygvhdqSORmN47\nnRo/D5E+wpGYHn39+KR3Tl7K0GhMj71+nC5KAAAAAIBnKFACSKgGSxflnubUGPP6xC+b9f1dp4x4\neVGOHr1rrXICfg+ySn8Bv0/fuM7sovz5gU4dsIxURHLsO9VnHWG5eX3mdQXbxgo3dw8mbPwhMo/t\nWpSs8a4SeyiROC81dSoWS1TvZLxozNWL+zuTcjYAAAAAAJdCgRJAQjXOt+yhTIFRn788dk5/8pMm\nIx7y+/TInWtVWZzrQVaZ44sNczW3NM+Ib2EX5YzZur3ZiDXUlSa1COOVotyg8fXmurKO8AQk+/7J\nZI13vchWoGQPJSbrhf0dxujuRBkIR/ViU0dSzgYAAAAA4FIoUAJIqIY6s4Nyb0tv0j79PxGnegb1\nwNbdilpy+JNbV2mtpaiKyQkFfLp/U70R/4f32nWsi6JRsvUNjerH+9qM+Ob1dR5kMzPseyjp2IVd\nU5ulgzLJBcorF5gFyp0ne6w/i4Dx7GlJ7hSK3Uk+HwAAAACA8VCgBJBQSyqKVDhm313/SMSzItVQ\nOKr7ntyl7oGw8dhdn5ivr2VwAWemfWXdPJUXxe9zc13p4VePeZRR9nh+z2kNj8bvESvKDehzV9R4\nlFHysYcSE+W67oyPeJU+6NAsGvPz8MJIhNHXmJT2vuHknt+b3PMBAAAAABgPBUoACeX3OVo9r8SI\ne/EJfdd19fvPvav9ls6Z9Qtn648/v3LGc8pkuUG/7ttodlE+v/e0WrsHPcgoO7iuq63bW4z4bY21\nygtl7l7VZZVmgfJQJ926MJ3uHdL54UhcLDfo04KygqS+r9/naN0Cs0OfPZSYjEiSO26TfT4AAAAA\nAOOhQAkg4RrmWfZQtsz8HsrHth23jr2sKcnVw3c0KujnEphomzfUaVZ+MC4Wjbn6y9fpokyW3S09\nOmTpHNy8IbO7g5daCpSHO+ighMk23nV5VbH8Pifp771+YZkR23HiXNLfF5kjkOSv02SfDwAAAADA\neLg7DyDhbHsoZ7pA+frhLv3Zzw4a8ZyAT4/etU5zCnMsr8J05YcCuueahUb8BztPqb1vyIOMMt/T\nb5vdk1cumGUt4GWSRRUFRoGp4/yw+gZHPcoIqepAu1m4TvZ414vWLzT3UO440S3XpWsNE1Ndkpvc\n80uTez4AAAAAAOOhQAkg4RrqzA7Kw2f6dX54ZgoHJ88O6Jtbd8s2tezPbrtCl9eaI2iROHdftUBF\nufF718LRmB7bdtyjjDJX72BYP3mv3YhnevekJOUE/Fo4xxzRaesmRXZrau8zYiuqZ6ZAefncEuUG\n43/d7hkc1dEzjCPGxNh+p0qkxiSfDwAAAADAeChQAki42QUhLSjLj4u5rvRuq3mTONEujET09Sd2\nGvvGJOnr1y7UFxvmJj2HbFecG9RvX7XAiD+zo0Vd/SMzn1AGe273aYUjsbhYaX5Qv3FZtUcZzSz7\nHkoKlIjX1G6OeF05QwXKUMBnLQCxhxITdcuqKhUkaZ9wQcivm1dWJeVsAAAAAAAuhQIlgKSwfeJ/\nT0tPUt8zFnP1u8/u1RFLZ8q1S+bo9z+9PKnvj1/7J1cvVP6YG6rDozE9/uYJjzLKPK7rauv2ZiP+\n5cZa5QaTczM71bCHEpdyfnhUrd3x46UdR1peNXMjkMcb8wpMxE0rK+VL0p5Iv8/Rzasqk3I2AAAA\nAACXQoESQFI0WvZQ7k5ygfI7rxzVi02dRrxudr6+c3uDAn4ueTNldkFId35ivhF/8pcn1TsYnvmE\nMtD2E9061jVgxG/PgvGuFy2zFJkOUaDERxy07J9cUFaggpyA5dnJwR5KTEco4NN9m+qVl+APnuQF\nfbp3U72C/G4EAAAAAPAI/yIFkBTWDsrW3qTdkH2pqVP/6+eHjXh+yK/H7l6r0vxQUt4X4/tn1y5U\nKBD/Y2YgHNXfvHXSm4QyzNbtLUbsE/Wztai80INsvGEtUHb2U/jBrzS1maPFZ2q860UN82Yp6I/v\ngOs4P2x0dgLjuX/jItWU5ipRfZSOI9WU5un+jYsSdCIAAAAAAJNHgRJAUiyrKlJuMP4S0zs4qpPn\nBhP+XkfP9OtfPrvX+thffGW1llfN7M1ofKCiKFe3XznPiP/NWyfUPzzqQUaZo3sgrJ+932HE79hg\ndq1msrrZ+coZUwTvGxrVGXad4kMHLB2UK2tm9mdCXsivK2rNqQLbT5yb0TyQvgJ+nx65c63yErSL\nMj/o1yN3rmWyBAAAAADAU/yrFEBSBP0+6w3Z3c2JHfPaNzSqrz+xSxdGIsZj37xhsX7j8uqEvh8m\n595Ni4zOofPDET35trk7ERP3g12tCkdjcbGygpBuWVXlUUbe8PscLak0O0YZ84qLmtrPG7EV1TO3\nf/Ii9lBiupZUFunJe9arIOSfcielI6kg5NcT92zQEssOXwAAAAAAZhIFSgBJ02DZQ7mnNXEFymjM\n1b/47h6dOGvu4btxeYX+5aeWJuy9MDVzS/N0W2OtEX/8jRMaCkc9yCj9xWKudbzrl9fVGiN1DAuk\nkQAAIABJREFUs8GySrMbjgIlJCkSjelQp6WDsrpkxnOxFSjfOUmBEpOzdv5sPf/A1aouyZ30a/OC\nPtWXF+j5B67W2vnmGH4AAAAAAGZa9t3JBDBjGuZZ9lC29Cbs/L948ZBeO9RlxOvLC/S/vrZGPl+i\ntjVhOn7nukUa+1dxbiCsZ3aYRTZc2i+Pn7OOSr79yjoPsvHesipLB6WlKIXsc/zsgMKR+E7j2QUh\nVRbnzHgua+fPMq6DJ88NqvP88IzngvS2pLJIN6+ceLd8XtCn4tyAHrxhiV741kY6JwEAAAAAKYMC\nJYCkabR0UB7s6Ndg2BzHOlk/ebdND792zIgX5QT0V3evU3FucNrvgcSYX1agW9fMNeKPbjumkQhd\nlJNl6568dskcLZhT4EE23ltqudl+mAIlJDW12ce7Os7Mf3ilODdo3X3JmFdMluu6euXQGSM+uyBk\nHf36xYZa7fqjm/TA9YvZOQkAAAAASCn8KxVA0lQU52puaV5cLBpz9e6pvmmd29R2Xv/m++8acceR\n/v/b12hRudlRBW9947pFGlsT6Dw/oh/sOuVNQmnqTP+wXtjfYcQ3r8/O7klJWlZlL1DGYq4H2SCV\n2PZPrqw2i4QzZf2CMiNGgRKTdfTMBbV0x3fR+xzp5d/dpH9+4xLj+X7fB3vBAQAAAABINfxrFUBS\nWfdQTmPMa/dAWPc+uVNDo2bn3b+6aaluWF455bORPEsqi/Qbl5kj6f7ytWMajcYsr4DN93eeUmRM\n4a28KEefWpm9X/dVxbkqzg3ExYZHY8YNfGSfA7YCpaWLcabY9lBSoMRkvXSg04itmz9bswpCWmEp\nwB9op6McAAAAAJCaKFACSKrGOnMP5e6WnimdFYnG9ODW3TrVM2Q89pnLq/TA9YundC5mxjeuM/9+\nTvUM6Ud72zzIJv3EYq6++4453vWr6+ZldXeM4zjWLkr2UGY313XHGfHqXYHyygXmz8NDnf3qGQh7\nkA3S1csHzPGuN66okCStshTgD7Sfp6McAAAAAJCSsveOJoAZMV4HpetO/mbZn/70oH5x7JwRX15V\npP/+5dWe7BXDxF02t0Q3LK8w4g+/elRRbp5e0htHz6q1O7447zjS19bP8yij1GHdQ9lBgTKbnekf\n0bkxhb+Q3+fpCPCywhwtqTDf/52TdFFiYs5dGLF+yOtiF33trDwV5cR3lA+Go2qmoxwAAAAAkIIo\nUAJIqpU1xQqN6e46e2HE2gX5cZ7bdUp//dYJI16SF9Rjd61TwZgbckhNti7X42cH9NP32j3IJr08\n/XazEdu0tFy1s/I9yCa10EGJsWz7J5dWFXrebcyYV0zHKwfPaOznuxbOKfhV4d1xnHHGvJrfDwAA\nAAAAeI0CJYCkygn4ddlc82bZZMa87mvt1b/9u/eMuM+RtmxuVF0ZBZp0sXb+LF29uMyIP/TKUUbQ\nfYzO88N6+aA51m/z+joPskk9yywdlIfooMxq1vGuVd6Nd73IWqCkgxITZBvv+qkV8ZMJbHtWbd8P\nAAAAAAB4jQIlgKRrsOyh3NPSO6HXnukf1n1P7lI4EjMe+8PPrNA1S+ZMOz/MrAevX2LEDnX26+cH\nOj3IJj08+06rMQa3qjjXOjI3G9lGvJ44O6CRSNSDbJAKbB2UtsLNTLMVKN8/3acLIxEPskE6GR6N\natuRLiN+44rKuP+90tJBaft+AAAAAADAaxMqUDqO82nHcQ45jnPUcZw/sDxe5zjOq47j7HEc513H\ncT7zYXy94zh7P/y/fY7jfCnRfwAAqc+6h7L10gXKcCSmbzy1Wx3nh43HvtQwV/dcszAh+WFmfaJ+\nttbNN4vWD716dEq7STNdNObquztajPhXr5yngMfjKlPFrIKQKopy4mKRmKsTZwc8yghes420tBVu\nZlp1SZ7qZsd3/cdcaVfzxKcKIDu9ffycBsPxH7ooyQsaP08Z8QoAAAAASBeXvLPpOI5f0hZJvyFp\npaTbHcdZOeZp/17S91zXbZD0NUkPfxh/X9I613XXSPq0pEcdx2FRHJBlGi0dlE1tfRoe/fjupv/4\n9/u103LT9rK5xfpvv3m5HMdJWI6YOY7j6MEbzF2U757q07YjZz3IKLW9duiM2vrii/Q+R/ra+nke\nZZSarHsoGfOalQbDEWtxenkKFCil8fZQnvMgE6QT25SB65eVGx9UWVJZKL8v/vej9r5hdQ+Ek5of\nAAAAAACTNZHWi/WSjrque9x13bCk70q6dcxzXEkX7/qUSGqTJNd1B13XvTizKvfD5wHIMtUluaos\nju9uGo262t/WN+5rnt7erK3bza6xsoKQHr1rnXKD/oTniZmzaWm5Lp9bYsS3vHLUg2xSm+374Ibl\nlaouyfMgm9TFHkpcdLCjX2ObsWtn5akkL+hNQmPYC5TsocT4XNe17p8cO95VknKDfi0uLzTidFEC\nAAAAAFLNRAqUcyW1fuR/n/ow9lH/UdKdjuOckvRTSd+8+IDjOBscx9kv6T1J93+kYAkgSziOo9Vz\nzTGvt/3lL7X4D3+qa//sFf3zZ/boH95t12g0pp0nu/Uff7zfeH7A5+jhOxo1t5TCTLobr4tyx8lu\nbT9OJ9FFp3uH9Ooh86b0HRvqPMgmtS21dFAe7qRAmY1SdbzrRRssBcp9rZeeKoDstb/tvNrHdNIH\nfI42LSu3Pn9FtXk9pEAJAAAAAEg1iVpedbukv3Vdt1bSZyQ96TiOT5Jc193uuu4qSVdK+reO4+SO\nfbHjOPc6jrPTcZydXV1dCUoJQCoYjcb00KtH9PoR+/d2JOaqtWdIP97Xpt/7wT41/MlLuvvxHRqN\nmg3X/+HzK7WhvizZKWOG3LSi0tr19tCrdFFe9OyOFsXGfCvMLc3TxqX2m9LZzNpBSYEyKzW1mYUY\n214+r9TNzjd2poajMe2dwG5mZCdb9+SG+tkqzrV3Ba+sMb/ebd8XAAAAAAB4aSIFytOSPrroqvbD\n2EfdI+l7kuS67i/1wTjXOR99guu6ByRdkHTZ2DdwXfcx13XXua67rrycm65ApjjS2a9bvr1NW145\nppFI7JLPHwhHdWEkokFLF8nXrpynOz8xPxlpwiM+n6MHLF2Ubxw5qz0t5u7RbBOJxvTszlYj/rUr\n5xn7xfDB3rWxa2lbu4c0MMLghmxj7aC0FGy84jgOY14xKbb9k5+yjHe9aGW1OUK9iQ5KAAAAAECK\nmUiB8h1JSxzHWeg4TkjS1yT9eMxzWiTdKEmO46zQBwXKrg9fE/gwPl/SckknE5Q7gBS2q7lbt255\nSye6BjQ0zbF1jXWl+k+3rpIztvqAtPfZy6u1cE6BEd9CF6VePnhGnedH4mJ+n6OvXjlvnFdkt/xQ\nQHWz8404Y16zSzTm6qBl92gqjXiV7GNeKVDCpvP8sN47be7s/rgCpW3E69EzFzQSYYwwAAAAACB1\nXLJA+eHOyAclvSDpgKTvua6733GcP3Ec5wsfPu1fSfq64zj7JD0j6bdd13UlXSNpn+M4eyX9naRv\nuK57Nhl/EACp40hnv+56fIcGw1GZg1onx5H0e7csU07An4jUkGL8PkffuG6REf/5gTPa32bekM0m\nW7e3GLGbVlSqotiYlI4PLbWMeaVAmV2azw1oMBxfhCnKDah2VmrtLl6/0BxXvqu5R6PRS08bQHax\njXddVlmkeZYPZFxUVpijyuL4McKRmKsjnRcSnh8AAAAAAFM1oR2Uruv+1HXdpa7rLnJd979+GPtj\n13V//OH/3+S67tWu6652XXeN67ovfhh/0nXdVR/GGl3XfT55fxQAqWA0GtN9T+3SUDhxn9L/d8+/\nz03bDPbFhrmaW2oWDx5+9ZgH2aSG1u5BbbPsbd28oc6DbNKHdQ9lBzfks8mBdrMgvaK6OOU68JdU\nFKo0P35/4NBoVO9bOuWQ3WzjXW9cUXHJ19m6hhnzCgAAAABIJRMqUALARD267Zjae4en3Tl5kSup\nrXdIj27L3mJVpgv6fbrf0kX50/fbdfRMdna/PbOjRe6Yb6K62fm6ZvEc+wsgSVpaZSlQdnJDPps0\ntZsFvlQb7yp9sIP3ygWMecXHGwxH9NZRc/jMp1aOP971ohWWr3vbflYAAAAAALxCgRJAwoQjMT36\n+vFp75wca2g0psdeP04XZQb7ytpaVRTFj6Nz3ezsohyNxvS9naeM+O3r6+TzpVYXWKpZbitQ0kGZ\nVZrazAJMKhYoJfZQ4tLePHJWI5H4333mFIa0prb0kq9dWWPpoLR8fwAAAAAA4BUKlAAS5qWmTsVi\nieqdjBeNuXpxvznmDJkhN+jXvRvrjfiP9rWp+dyABxl556WmTp29MBIXC/odfWVdrUcZpY8FZQUK\n+uOLuGcvjOjcmP+eyFy2Ea+2Qk0qWG8rUJ7sVjRJP0eRfmz7J29YXjGhD6uMN+LVHdueDwAAAACA\nRyhQAkiYF/Z3aCCBuyc/aiAc1YtNHUk5G6lh84Y6zS4IxcWiMVePvJ5dXZRPb282YjevqtKcwhzL\ns/FRoYBP9XMKjfjhTroos8G5CyPqOD8cFwv4HC2uML8mUsHK6mIVhPxxsf7hiA51ZOdoa8SLxVy9\nfNAsUN644tLjXSVpflmB8oLm19fp3qGE5AcAAAAAwHRRoASQMHtaepJ6/u4knw9v5YcCuueahUb8\nB7tOqS1LbqiePDugt46eM+J3bKjzIJv0ZN1D2cFYw2xg655cVF6o3DFFmlQR8Pu01rqH0rwGIPvs\nO9VrdNOHAj5du2Riu4j9PkfLq83rIWNeAQAAAACpggIlgIRp7xu+9JOmc35vcs+H9+7+5HwV5wbi\nYqNRV49tO+5RRjPrmR0tRqx+ToE+WV/mQTbpybqHkg7KrHCg3bJ/MkXHu15k3UN5kj2UkH5+wBxr\nf/WiMuWHApZn24035hUAAAAAgFRAgRJAwkSSvDcr2efDe0W5Qf321WYX5TM7WtTVn9l7BEciUX1/\n1ykjfvv6OjnOpfeN4QNLK80C5eFORmZmA1vhxVagSSXWPZQnutkTCOv+yYmOd71oheXr31bIBwAA\nAADACxQoASRMwJfcIkqyz0dq+CdXLTD2so1EYvo/b2Z2F+XP3u9Q90A4Lhby+3Tb2lqPMkpPy2wF\nyo5+Cj5ZwDa60lagSSVX1JYoFIj/dfzshbBOnB3wKCOkgtbuQR207CK9cUXFpM6xdRDTQQkAAAAA\nSBUUKAEkTHVJbnLPL03u+UgNswpCuvOT8434U79sVs+YAl4m2brdHO/6mcurNLsg5EE26at2Vp7y\nxxS4+0ciakvyCGp4a3g0qmNd5ijfFZYdfKkkJ+BXw7xSI77jBGNes9nLlvGul80tVnVJ3qTOWV5V\npLEN+K3dQzo/PDqd9AAAAAAASAgKlAASpqFuVlLPb0zy+Ugd/+yaeuWM6SoaCEf1N7846U1CSXb0\nzAVttxQkNm8wC7X4eD6foyXjdFEicx09c8EYA15VnKuywhyPMpo46x5KCpRZ7eWD5njXT01yvKsk\n5YcCWlhWYMQPtnM9BAAAAAB4jwIlgIS5ZVWVMZozUQpCft28siopZyP1lBfl6Pb1dUb8b986kZGd\nH7buycUVhbpyAUX5qVhWWWjEDrGHMqPZx7umdvfkResXlhkx2wcWkB36h0f19vFzRnwqBUpJWmEb\n89rWN6WzAAAAAABIJAqUABLmppWV8iVpT6Tf5+jmVVO7OYf0dN+megX98V9P54cjevKXzR5llBzD\no1E9t/uUEd+8vk7O2Nl8mJCldFBmHdtePdv+vVTUOL/U2LF8undIp3oGPcoIXtp2+KxGo2Y38Kop\nfj2vtOxhZQ8lAAAAACAVUKAEkDChgE/3bapXXjCxXZR5QZ/u3VSvoJ9LVjapLsnTl9fWGvHH3zyh\nwXDEg4yS46fvtatvKL4rNCfg022N5p8dE7OsyixQHqRAmdGsBcrqEg8ymbz8UECXzTVzfeckXZTZ\n6OeW/ZM3rqiY8gdWbAXKA4x4BQAAAACkAO72A0io+zcuUk1prhLV9+U4Uk1pnu7fuChBJyKd/M6m\nxfKP6SzqHghbR6KmK9uf5XNX1KgkP+hBNpnBVqA82nVBkWjMg2yQbK7r6oClQJk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hTw+/T9r+xWSYp2UZYG/fr+V3YrQPdkTrLuoWTMa8YtNt7VcdIzkjmf7LEUKD+4OqGZhagH2eS/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\nZKN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13f3e9db38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(32,20))\n",
    "plt.plot(parameter_values, avg_scores, '-o', linewidth=5, markersize=24)\n",
    "#plt.axis([0, max(parameter_values), 0, 1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5N885N2/SpElJtCQiIsORTOgfAGaa2S1mNoZwsO8cvJOZ3QbcAOyLKdcCC83s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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13e73bc4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for parameter, scores in zip(parameter_values, all_scores):\n",
    "    n_scores = len(scores)\n",
    "    plt.plot([parameter] * n_scores, scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f13e77c5630>,\n",
       " <matplotlib.lines.Line2D at 0x7f13e77c57f0>,\n",
       " <matplotlib.lines.Line2D at 0x7f13e77c57b8>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13e80c0b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(parameter_values, all_scores, 'bx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13e80e6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "all_scores = defaultdict(list)\n",
    "parameter_values = list(range(1, 21))  # Including 20\n",
    "for n_neighbors in parameter_values:\n",
    "    for i in range(100):\n",
    "        estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "        scores = cross_val_score(estimator, X, y, scoring='accuracy', cv=10)\n",
    "        all_scores[n_neighbors].append(scores)\n",
    "for parameter in parameter_values:\n",
    "    scores = all_scores[parameter]\n",
    "    n_scores = len(scores)\n",
    "    plt.plot([parameter] * n_scores, scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f13e74cd4a8>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pBLYAz4nI8H2LyDLg28Bf+ruxMeYZY0y5MaY8J2dik3wq/BblpnAqhAyekw0O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\nWBiBFo5Ng75SSgXp0+sKEQksZ//l/bU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13e77d6080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(parameter_values, avg_scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "30px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
